Browsing by Author "Wang, Hai"

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    Design and Analysis of Uplink Transmission Performance Enhancement Methods for Data Collection in Internet-of-Things Networks
    (2020-04-28) Wang, Hai; Fapojuwo, Abraham O.; Messier, Geoffrey G.; Sesay, Abu B.; Williamson, Carey L.; Jiang, Hai
    With the increasing demands for the services provided by the Internet-of-Things (IoT) networks, tremendous efforts have been dedicated to enhance the performance of machine-to-machine (M2M) communications. However, due to the limited spectrum resources available for IoT networks, the uplink transmissions that are commonly used for data collection suffer performance degradation when the traffic load increases. To improve the uplink network performance under high traffic load, we propose new enhancement methods for the IoT networks with both single-hop and multi-hop configurations.Specifically, for single-hop networks, this thesis addresses three research objectives. First, successive interference cancellation (SIC) is implemented on top of the pure Aloha (PA) medium access control (MAC) mechanism. The problem is to perform the SIC under an unsynchronized packet transmission framework, and without introducing extra signaling overhead. To this end, a window-based SIC algorithm is presented for the network’s single gateway (GW). Second, in order to evaluate the performance of the SIC-based PA, a throughput model is developed and analyzed to study both the throughput and the packet delivery ratio (PDR) performance metrics. Third, the problem of enabling the SIC-based PA in an IoT network with multiple GWs is solved. The SIC algorithm for PA is redesigned to accommodate single-hop multi-GW networks. A throughput model is also proposed for the newly designed PA-based SIC in multi-GW networks. For the multi-hop IoT networks, the main research objective in this thesis is to allocate proper bandwidth for the nodes in the mesh networks. As a solution, a new distributed bandwidth allocation algorithm is designed. The proposed new design significantly improves the mesh network’s uplink transmission performance at high traffic load. Meanwhile, the new algorithm does not require the configuration of the hysteresis quantum, which makes it more practical than the current state-of-the-art distributed bandwidth allocation algorithms. The performance evaluation results obtained for both the single-hop and multi-hop IoT networks indicate that the proposed enhancement methods can significantly improve the uplink PDR, throughput, and latency for high traffic load scenarios.
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    Design of a Drone-Assisted Wireless Sensor Network System for Feedlot Animal Health Monitoring
    (2015-10-01) Wang, Hai; Fapojuwo, Abraham; Davies, Bob
    Cattle health monitoring on the feedlot is a crucial but nontrivial task. Conventionally, the monitoring quality relies heavily on the obviousness of the observed traits and the time spent on observing each animal, which make the early detection of the illness hard to achieve. In this thesis, a wireless sensor network system is developed to monitor the animals’ feeding and drinking behaviors thereby increasing the probability of detecting the animals with early signs of illness. Deploying mechanism of the drone is also proposed to localize the animal that needs care. Finally, a scheduling algorithm is designed to support data transmissions in animal wireless body area networks. The significance of the research lies in the novelty and feasibility of using directional antenna, drone and wireless body area networks for feedlot animal health monitoring.
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    Design of Low-toxic Non-solid Anti-freeze Polymer Drilling Fluid
    (2014-08-26) Wang, Hai; Martinuzzi, Robert
    Drilling in permafrost and at low temperature are two challenges that complicate Arctic operation. Addressing these challenges require new approaches in drilling fluid design and optimization. This experimental study proposes formulations for low-toxic anti-freeze agents and non-solid polymer system. Tests are conducted to verify that 5% NaCl + 5% KCl + 30% glycerol are effective to maintain the freezing point below -20°C. Experiments were performed on the influence of anti-freeze agent on the rheological and filtration control of polymer solutions. Three formulated polymer systems are demonstrated to be able to generate desirable viscosity and yield point to provide efficient hole cleaning at temperature range of -20°C ~ 0°C. The desirable filtration characteristics are shown by generating the minimized volume of filtrate. A rheological model describing the flow behavior of anti-freeze polymer fluids is selected and used to predict the frictional pressure loss in a simulated mud circulation.
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    Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches
    (2024-04-25) Wang, Hai; Chen, Shengnan (Nancy); Chen, Zhangxing; Gates, Ian Donald; Shor, Roman J; Zendehboudi, Sohrab
    The development of unconventional tight/shale gas reservoirs has undergone a revolutionary transformation, primarily fueled by advancements in horizontal drilling and multi-stage hydraulic fracturing technologies. These innovations have enabled the economical extraction of hydrocarbons from formations characterized by low permeability and complex fracture networks, positioning tight/shale gas as a pivotal component of the global energy mix. The accurate prediction of well production dynamics in these complex formations is a formidable challenge. Traditional empirical and numerical approaches, often fall short due to their inherent simplifications or computational demands. With the surge in data availability from unconventional reservoir developments, leveraging data-driven models for predicting well performance has become increasingly feasible and necessary. This thesis presents a comprehensive suite of machine learning frameworks to predict and enhance well production dynamics in tight/shale gas reservoirs. Initial efforts focus on predicting the first-year cumulative production of infill wells and optimizing their placement and stimulation design. Then, the study delves into the prediction of long-term production dynamics of shale gas wells using a dual-stage attention-based sequence-to-sequence model with some hard physics constraints. By encoding both tabular and time-series data, this model demonstrates superior accuracy and robustness in forecasting well production, outperforming traditional machine learning approaches. Subsequently, a novel physics-informed neural network approach is introduced to deduce the governing partial differential equation for shale gas production decline characterization, integrating Caputo fractional derivatives to capture the heavy-tailed phenomena in production series, thus offering a balance between interpretability and predictive capability. Further, the thesis explores the competitive adsorption of CH4/CO2 in shale formations using a Genetic Algorithm pruned Neural Network. This model robustly predicts the adsorption capacities, offering critical insights for CO2-enhanced shale gas recovery strategies and contributing to carbon capture and storage efforts.